Context in word recognition

摘要

Relatively low character error rates can often lead to prohibitive levels of word error rates. This paper examines several techniques for integrating an independent contextual postprocessor (CPP) into a full classification system. Using positional binary n-grams the CPP can correct many errors directly. In those cases where the correction process leads to ambiguity, the CPP can direct additional processing. Experimental results demonstrate that almost all of the derived improvement results from CPP-directed reclassification. This only requires that the CPP have the classifier likelihood fed forward to it. Therefore, a standardized CPP can be built independently of the rest of the classification system. An initial 45% word error rate is reduced to about a 2% word error rate and a 1% reject rate. Presence of a dictionary allows these figures to be reduced even further.